What is it about?
The dynamics of the entire recommender system includes shifts in both user interest and item availability. We propose a simple yet effective framework with three key perspectives, tailored to the dynamics of recommender system by fully exploiting the time information.
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Why is it important?
We consider the dynamics in both user and item perspective in recommender system and exploit the time information. Our framework is effective and generalizable.
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This page is a summary of: Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3627673.3679955.
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